Breaking Down Corn Improves Hyperspectral Image Analysis

Hyperspectral imaging once was used primarily in satellite-based remote sensing applications, but it is beginning to gain momentum for tasks closer to the ground.

Hyperspectral cameras normally collect reflectance or transmission data from wavelengths beyond the visible light range, and this spectrum is divided into up to hundreds of bands. This means that each hyperspectral image has hundreds of virtual layers, and that each pixel is associated with a particular hyperspectral profile that can act as a fingerprint because different objects have unique profiles. Additionally, two similar objects subjected to diverse conditions (such as the growth conditions of two genetically identical plants) will have different profiles. As such, the technique is as suitable for studying food in the laboratory as it is for researching mineral deposits or crops from orbit. Scrutinizing foodstuffs with hyperspectral imaging, however, requires analytical techniques that have not yet matured.

To bolster these methods, researchers at Texas A&M University in Lubbock performed hyperspectral imaging analysis on corn samples, determining the effects of preprocessing the samples and of spectral filtering the data. The investigators — Christian Nansen, Michael Kolomiets and Xiquan Gao — were especially concerned about the extent to which the variability of shape among the kernels or fragments of the corn affected the spectral data. They also tested whether filtering helped hyperspectral imaging distinguish between two types of corn that were nearly identical genetically.

The group used a hyperspectral camera made by Resonon Inc. of Bozeman, Mont., to acquire spectra from whole kernels, from fragments ranging in size from 0.250 to 0.354 nm (dubbed “size 1”) and from particles 0.354 to 0.841 nm in size (size 2). The samples were 5 g each, and 10 images were acquired for each combination of genetic line and particle size. The camera collected spectra from 160 wavelength bands between 435 and 769 nm with a resolution of less than 3 nm. Each resulting hyperspectral image cube had 250 frames of 640 pixels each. The researchers used a ringlight mounted 25 cm above the samples to provide controlled illumination.

They found that there was a distinct difference in the hyperspectral profiles acquired from whole kernels compared with those from the processed fragments, as well as between the two size groupings. Differentiating between the two genetic types of corn worked best at 481 nm for whole kernels, at 505 nm in size group 1 and at 509 nm for fragments in size group 2. Grinding the kernels reduced the overall variance among the hyperspectral profiles, largely because doing so reduced reflectance aberrations. The scientists also reported that analyzing the particles in size group 1 provided the best classification data.

Validation of the data analysis showed that distinguishing between genetically different corn types — even if the types differ very little — could be accomplished with more than 80 percent accuracy.

According to the investigators, the technique easily could be scaled up from 5-g samples to large volumes of grain and can be used to analyze most other food, including meat products.